AIMC Topic: Skin Neoplasms

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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

European journal of cancer (Oxford, England : 1990) Aug 14, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Neural Networks, Computer Algorithms Female Melanoma Humans Surveys and Questionnaires Diagnosis, Differential Biopsy Dermatologists Skin Neoplasms Dermoscopy Sensitivity and Specificity Male Hospitals, University Nevus
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Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data.

European journal of cancer (Oxford, England : 1990) Aug 8, 2019
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its...
Nevus, Pigmented Skin Neoplasms Dermoscopy Sensitivity and Specificity Early Detection of Cancer Mutation Skin Cell Transformation, Neoplastic Artificial Intelligence Humans Algorithms Prospective Studies Melanoma
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Deep neural networks are superior to dermatologists in melanoma image classification.

European journal of cancer (Oxford, England : 1990) Aug 8, 2019
BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level preci...
Algorithms Melanoma Skin Neoplasms Dermoscopy Humans Neural Networks, Computer Skin Dermatologists Image Interpretation, Computer-Assisted Reproducibility of Results Nevus Surveys and Questionnaires ROC Curve Biopsy
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Support Vector Machine Classification of Nonmelanoma Skin Lesions Based on Fluorescence Lifetime Imaging Microscopy.

Analytical chemistry Jul 31, 2019
Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to precisely evaluate the development stage of nonmelanoma skin cancers because they are derived from the sa...
Support Vector Machine Microscopy, Fluorescence Eosine Yellowish-(YS) Hematoxylin Humans Skin Neoplasms Fluorescence Time Factors
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Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.

Journal of medical systems Jul 20, 2019
Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign - depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by...
Sensitivity and Specificity Humans Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Melanoma Color Skin Neoplasms Dermoscopy
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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Image Interpretation, Computer-Assisted Observer Variation Humans Nevus Melanoma Predictive Value of Tests Skin Neoplasms Diagnosis, Computer-Assisted Biopsy Reproducibility of Results Deep Learning Pathologists Diagnosis, Differential Microscopy
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The Application of Deep Learning in the Risk Grading of Skin Tumors for Patients Using Clinical Images.

Journal of medical systems Jul 13, 2019
According to diagnostic criteria, skin tumors can be divided into three categories: benign, low degree and high degree malignancy. For high degree malignant skin tumors, if not detected in time, they can do serious harm to patients' health. However, ...
Area Under Curve Melanoma, Cutaneous Malignant Patient Participation Cell Phone Databases, Factual Melanoma Humans Skin Neoplasms Neural Networks, Computer Deep Learning
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Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.

Journal of the American Academy of Dermatology Jul 12, 2019
BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.
Keratosis, Seborrheic Humans Melanoma Skin Neoplasms Spain Dermatologists ROC Curve Dermoscopy International Cooperation Israel United States Cross-Sectional Studies Internship and Residency Colombia Diagnosis, Differential Deep Learning Nevus Image Interpretation, Computer-Assisted Skin
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Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.

Journal of medical systems Jul 4, 2019
Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melan...
Skin Neoplasms Support Vector Machine Humans Algorithms Melanoma
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Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

Journal of the American Academy of Dermatology Jun 27, 2019
BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards.
Sensitivity and Specificity Melanoma Skin Neoplasms Humans Dermoscopy Artificial Intelligence Reproducibility of Results Retrospective Studies
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